Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API.
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650,440 tools. Updated 2026-10-13 07:03
"A server or tool using RAG for documentation scraping, storage, and retrieval with SSE support" matching MCP tools:
- Use this when the user asks to see, choose, or look up their documentation Spaces, especially before using tools that need a numeric space_id. Read-only: returns accessible Spaces and does not change settings or documents.ConnectorOAuth
- Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.ConnectorNo auth
- Opens a persistent SSE connection that emits events as the task progresses. The stream closes automatically when the task reaches a terminal state or after ~90 seconds (timeout). Heartbeat comments are sent every ~15 seconds to keep the connection alive through proxies. Event types: - `status` — emitted when status changes (pending → running → complete/failed) - `result` — emitted on `complete` with the full result payload - `error` — emitted on `failed`, `cancelled`, or `expired` with error info - SSE comment (`: heartbeat`) — keepalive, no data Use this tool when: - You want real-time progress without polling. - You are in an environment that supports SSE (EventSource API). Do NOT use this tool when: - You want a simple one-shot status check — use `get_task` instead. - Your HTTP client doesn't support streaming responses. Inputs: - `task_id` (path, required): 26-char ULID. Returns: - SSE stream (`text/event-stream`). Each event is `event: <type>\\ndata: <json>\\n\\n`. Cost: - Free. Counts as one request against rate limits when the stream opens. Latency: - First event: <200ms. Stream duration: up to 90s.ConnectorNo auth
- MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.ConnectorNo auth
- MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.ConnectorNo auth
Matching MCP Servers
- FlicenseAqualityDmaintenanceEnables retrieval and cleaning of official documentation content for popular AI/Python libraries (uv, langchain, openai, llama-index) through web scraping and LLM-powered content extraction. Uses Serper API for search and Groq API to clean HTML into readable text with source attribution.14-
- AlicenseBqualityDmaintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.1Apache 2.0
Matching MCP Connectors
SSE Accept hint
- web-scrapingOAuth
Web scraping for AI agents: scrape, search, crawl, map any website to markdown + JSON. No browser.
- List an EXTERNAL remote MCP server you run as a marketplace LISTING — for an MCP server hosted on YOUR OWN infrastructure that buyers connect their client straight to (FindAgent never proxies or runs it). Pass the listing basics (title/slug/tagline/description + example_prompts: 1–5 required; a listing is not categorised, so any category you send is ignored) and the remote endpoint as `server_url` (https) OR a parsed `server.json` object in `server_json`. The server's tools are auto-detected (a sandbox-gated live scan when available) — you can override with `tools` (name+description), `transport` (streamable-http|sse), and `auth_note` (what credential the server needs — NEVER a secret value). Creates a status=draft agent you own; then call findagent_submit_for_review IN THIS MCP CLIENT to submit it. The server URL is stored + displayed only; nothing executes on FindAgent. Before calling: findagent_check_slug + findagent_list_categories.ConnectorOAuth
- Send feedback about the Redpanda documentation or products directly to the Redpanda team. If the user hits a bug, a documentation gap, incorrect or missing information, or expresses frustration while using Redpanda, ASK whether they'd like to send feedback to the Redpanda team. Only call this tool once the user agrees — never submit feedback without their consent. When telling the user what will be sent, note that their work email and company domain are attached so the team can follow up. Summarize their feedback clearly and include the relevant documentation page URL or context when you know it. This tool requires the user to be signed in with a Redpanda Cloud account, because feedback the team cannot reply to is not useful. If the call is refused with authentication_required, the MCP connection itself is unauthenticated: tell the user to reconnect this server so their client runs its sign-in flow (signing in at https://docs.redpanda.com/login in their default browser first makes that hop quicker), and do not retry the tool until they have. Signing in on the docs website alone does not authenticate this connection.ConnectorDestructiveOAuth
- Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). IMPORTANT: For predictive and analytical tasks (forecasting, anomaly detection, key driver / root cause analysis, classification, churn prediction, or text generation), ALWAYS execute computation in-warehouse using BigQuery native AI/ML functions (`AI.FORECAST`, `AI.DETECT_ANOMALIES`, `AI.KEY_DRIVERS`, `AI.CLASSIFY`, `AI.GENERATE`) rather than exporting raw rows to a local Python sandbox. In-warehouse execution scales to billions of rows, preserves governance, and eliminates data egress latency. Example Queries: ```sql -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) -- Detect anomalies in time series data using AI.DETECT_ANOMALIES SELECT * FROM AI.DETECT_ANOMALIES( TABLE `project.dataset.historical_metrics`, TABLE `project.dataset.recent_metrics`, data_col => 'num_requests', timestamp_col => 'timestamp' ) -- Identify key drivers of metric changes using AI.KEY_DRIVERS SELECT * FROM AI.KEY_DRIVERS( TABLE `project.dataset.sales_summary`, metric_col => 'total_revenue', dimension_cols => ['region', 'product_category'], interest_label_col => 'is_current_quarter' ) -- Classify text into categories using AI.CLASSIFY SELECT ticket_id, AI.CLASSIFY(ticket_text, ['Billing', 'Technical Support', 'Feature Request']) AS category FROM `project.dataset.support_tickets` -- Generate text or summaries using AI.GENERATE SELECT review_id, AI.GENERATE(CONCAT('Summarize this customer review: ', review_text)).result AS summary FROM `project.dataset.reviews` ``` Queries executed using the `execute_sql_readonly` tool will always have the job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.ConnectorNo auth
- List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.ConnectorNo auth
- USE WHEN looking up an exact Pine Script API term or known concept keyword. Returns the best-matching doc paths with matched keywords and a retrieval suggestion (get_doc or list_sections + get_section). AFTER calling this tool, follow the suggestion: call get_doc() for small files or list_sections() + get_section() for large files. For natural language questions use search_docs() instead. Data sourced from bundled TOPIC_MAP and doc file content scan.ConnectorNo auth
- Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge. ## Follow-up Pattern To ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results.ConnectorNo auth
- Server-detected events from the last hour: funding outliers (≥3x 7d baseline), whale trades (≥$100k), OI caps reached. Cursor-based — pass next_cursor back as since_id to receive only new events. The polling equivalent of the /sse/signals stream. Pro tool get_signal_history covers 7 days with forward-return outcomes.ConnectorNo auth
- List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.ConnectorNo auth
- Returns JSON with the GodPrompt content version, MCP server version, repository, bundled file sizes and purposes, and task-type/protocol catalog. Use it to verify which GodPrompt content/server version a client is connected to; use the content retrieval tools when you need the actual workflow text.ConnectorNo auth
- Prepare a NEW free-signature document from one PDF without sending it. Accept Base64, a public HTTPS download URL, an existing SELISE storage file ID, a reference from the binary upload endpoint, or a ChatGPT attachment through chatgptFile. New bytes are uploaded; existing storage/upload references are reused. Use when preparation is requested or placements are not ready; do not call again for an existing or pending document. Provide exactly ONE file source: documentBase64 (raw standard Base64), documentUrl (public HTTPS PDF download URL, including a temporary signed URL), storageFileId (an existing SELISE storage PDF accessible to this server), uploadId (the exact short-lived reference returned by this server's HTTP POST /uploads), or chatgptFile (the host-provided file object for a PDF attached in ChatGPT). In ChatGPT, use chatgptFile via the declared file-input integration for a user attachment; its download_url and file_id are supplied by the host. Do not combine sources, invent file content or IDs, or pass local paths to the server. A bare ChatGPT attachment ID is not a documentUrl or storageFileId. Other clients can continue using the four generic sources. For binary upload, POST multipart/form-data with one file part named file to /uploads on this server's HTTP deployment, then use the returned uploadId before expiresAt; uploading does not prepare or send a document. Raw application/pdf uploads with a fileName query parameter are also accepted. The uploadId is valid for one hour; keep it private. Source PDFs are validated consistently. documentUrl must return PDF bytes without additional authentication headers; the server does not use browser cookies, login pages, or private-network URLs. It follows at most three public HTTPS redirects. Storage files and upload references are reused without a second upload. Supply fileName ending in .pdf and the normal preparation fields for every method. A file reference is not a documentId and does not mean preparation has completed. Maximum actual PDF size for every source on this server: 26214400 bytes, excluding Base64 encoding or multipart overhead. PDFs must be readable, unencrypted, and have supported page geometry. File-source retrieval and validation happen before preparation. Never fabricate file content. ownerEmail must match one of the signatories' emails. Signatory emails must be unique (case-insensitive). Provide a positive integer signingOrder for every signatory or omit it for all; role defaults to signer. Ask for missing participant details; do not invent people or change their intended roles to satisfy validation. Role values and behavior: signer signs the contract; reviewer is a view-only participant who can see the contract but has no action to perform; approver reviews and approves the contract without signing. Map a requested viewer to reviewer, and a requested reviewer who must approve to approver. The literal value viewer is not accepted. If the intended action is unclear, ask before choosing a role. No coordinates are needed for this tool. Returns prepared only after preparation is confirmed, or pending after acceptance without confirmation. prepared does not mean sent or signed. Once prepared (or preparation_success is observed), use send_prepared_signature_document with the returned documentId/fileId and verified placements when sending is requested. Keep returned documentId, fileId, operationId, and trackingId when present. pending means accepted but completion is unconfirmed: check get_signature_document_status with documentId instead of resubmitting. UNKNOWN_OUTCOME, a timeout, or a lost response may follow a successful submission; do not automatically repeat a preparation or send call. Inspect status when an ID is available; an empty status list does not make a retry safe. INVALID_REQUEST means inputs need correction; REQUEST_FAILED gives a referenceId for diagnosis. If the cause is unclear, ask for clarification or use the referenceId rather than guessing. Repeating a preparation can create another document. Use only the declared fields. Ask for missing facts instead of inventing values. Service credentials are configured on the server and must not be included in tool arguments.ConnectorNo auth
- Search current first-party OpenAI, Microsoft Learn, AWS, and Cloudflare documentation in one call. Use this as the default documentation research tool for questions involving any of those providers, especially comparisons or cross-cloud architecture. Select only relevant sources when the provider is known; omit sources to search all four in parallel. Returns each provider result separately with partial-failure reporting and provenance. No account or API key is required.ConnectorNo auth
- Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape.ConnectorNo auth
- USE WHEN discovering what Pine Script v6 documentation is available. Returns a categorised list of doc file paths with one-line descriptions. AFTER calling this tool, call get_doc(path) for small files or list_sections(path) then get_section(path, header) for large files (ta.md, strategy.md, collections.md, drawing.md, general.md). Data sourced from bundled Pine Script v6 documentation.ConnectorNo auth
- Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.ConnectorNo auth
- Search ALL JobMojito documentation. This is the single entry point. One call searches both documentation sources in parallel and returns a merged, source-labeled list — you do not need to choose a source or call a separate tool: • "developer" — developer.jobmojito.com: API reference, request/response schemas, tables, webhooks, code examples, integration guides. • "help" — help.jobmojito.com: recruiter, candidate, and administrator product guides (how the platform behaves for end users). Use this whenever you need to understand how a feature, endpoint, field, or workflow works — including before calling an action tool you're unsure about. Then call `get_documentation(url)` with a returned URL to read the full page.ConnectorNo auth